ArticleNPJ science of learning2024
Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking.
Article in NPJ science of learning, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence in robotic urologic surgery: a scoping review.World journal of urology · 2026Article
- The Suture-Action-Gaze Efficiency (SAGE) guidance framework reveals expert gaze-action coupling based on a case study of simple interrupted suturing on an artificial skin pad.Scientific reports · 2026Article
- Multimodal deep learning for objective skill assessment in robot-assisted vesico-urethral anastomosis.Journal of robotic surgery · 2026Article
- From precision to strength: computer vision for suture quality assessment-an ex vivo pilot study.Surgical endoscopy · 2026Article
- An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload Evaluation in Surgery.Human factors · 2025Article
- Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning.Surgical endoscopy · 2024Article
- Prediction of Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis using electroencephalography, eye-tracking, and machine learning.Scientific reports · 2024Article
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6 authors.
Funding
Abstract
The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R
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